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相关概念视频

Understanding Memory01:19

Understanding Memory

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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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System of Memory01:23

System of Memory

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Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
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Storage01:23

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Block Diagram Reduction01:22

Block Diagram Reduction

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The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
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相关实验视频

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Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
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动态模式分解与内存的分解.

Ryoji Anzaki1, Kei Sano2, Takuro Tsutsui3

  • 1Advanced Engineering 1st Department, Digital Design Center, Tokyo Electron Ltd., Akasaka Biz Tower, 3-1 Akasaka 5-chome, Minato-ku, Tokyo 107-6325, Japan.

Physical review. E
|October 18, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了动态模式分解与内存 (DMDm),一种新的数值方法来分析时间序列数据与内存效应. 这种方法克服了传统动态模式分解 (DMD) 的局限性,使复杂系统的分析成为可能.

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科学领域:

  • 应用数学 应用数学 应用数学
  • 数据科学数据科学数据科学
  • 工程物理学 工程物理

背景情况:

  • 动态模式分解 (DMD) 是一种强大的数据驱动方法,用于分析时间序列数据.
  • 标准的DMD依赖于普通微分方程,限制了其用记忆效应建模系统的能力.
  • 由于系统与环境的相互作用而产生的记忆效应在物理学和工程学中很常见.

研究的目的:

  • 开发一个数值方法,动态模式分解与内存 (DMDm),以分析包含内存效应的多维时间序列数据.
  • 为了克服传统的DMD方法固有的无记忆限制.
  • 用微积分计算来证明DMDm在分析具有权力定律记忆效应的系统中的实用性.

主要方法:

  • 制定了DMDm的抽象算法结构.
  • 使用卡普托分数导数实现DMDm来建模权力定律记忆效应.
  • 开发一个分数DMD方法,允许任意顺序的微分运算.

主要成果:

  • 成功地将拟议的分数DMD方法应用于来自分数振荡器的合成数据.
  • 对表现出记忆效应的系统的模型参数进行准确估计.
  • 展示DMDm能够使用内存处理时间序列数据的能力.

结论:

  • DMDm提供了一个强大的框架来分析时间序列数据与内存效应,扩展DMD的功能.
  • 分数DMD方法使得权力定律记忆现象的建模成为可能.
  • 拟议的方法在机械,热和流体系统的模型估计,控制和故障检测方面具有很大的应用潜力,特别是在先进制造领域.